Large-scale software projects remain the most challenging environment for AI-assisted development.

AI Coding—often referred to as Vibe-Coding—has already proven highly effective for:

  • Rapid prototyping
  • Exploring ideas and directions
  • Reducing the cost of going from zero to one

This is why one-liner Vibe-Coding—a single, continuous prompt–response flow—works extremely well in early exploration.

However, once we try to apply Vibe-Coding to large, long-lived, multi-team projects, the limitations quickly surface:

  • Outputs become inconsistent
  • The same prompt produces different results at different times
  • Code may compile and run, but its intent is unclear
  • Teams hesitate to merge and rely on intuition instead

Applying Vibe-Coding at scale is very challenging.

The Real Problem: Lack of Explainability

When AI Coding starts to break down, the symptoms often sound like this:

  • “Something feels off.”
  • “It works, but I don’t trust it.”
  • “I can’t explain why this solution looks the way it does.”

These are not engineering statements.

The core issue is not model capability. It is the absence of explainability.

Explainability is not about understanding the AI’s internal reasoning. It is about making AI behavior and outcomes understandable, predictable, and reviewable by humans.

Why SMART?

SMART is chosen because it is simple, widely understood, and structurally complete, while mapping naturally to the most common failure modes of AI Coding.

For AI Coding, time is rarely the real constraint. Iteration cost is.

So we adapt SMART into:

S M A R I — replacing Time-bound with Iteration-based

S — Specific (Outcome, Not Implementation)

In AI Coding, Specific means being explicit about the product-level outcome we want to see.

Not specific: “Build a new feature.”

Specific and explainable: “Produce an interactive prototype that clearly communicates what this feature does.”

M — Measurable (Acceptance Criteria, Not KPIs)

Measurable refers to product-level acceptance criteria:

Can we clearly see what functionality will exist in the product, and how users are expected to interact with it?

A — Achievable (Human Pre-Assessment Before Coding)

Achievable means: Have humans decided that this task is suitable for AI Coding in the first place?

Only after confirming that the task is appropriate should Vibe-Coding begin.

R — Relevant (Decision-Oriented Output)

Every AI-generated result must support a concrete decision:

What decision does this output enable?

If no decision follows, the output is a demo—not engineering.

I — Iteration-based (Bounded Exploration)

Each Vibe-Coding task should define an explicit iteration budget:

  • 3 iterations to validate a UI or interaction concept
  • 5 iterations to converge on a feature flow
  • 1 iteration to decide whether to abandon the idea

Once the limit is reached, a decision must be made: reset, change approach, or stop.

Conclusion

Applying SMART (SMARI) to Vibe-Coding is not about controlling AI. It is about building explainability—so we know what we are building, how users will interact with it, and when to stop.

In large projects, AI does not need to be perfect. It needs to be predictable enough to trust.

That is what makes Vibe-Coding scale.